A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Recognition of Local Features for Camera-Based Sign Language Recognition System
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
An animated Auslan Tuition System
Machine Graphics & Vision International Journal
Using optical flow for step size initialisation in hand tracking by stochastic optimisation
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Definition and recovery of kinematic features for recognition of American sign language movements
Image and Vision Computing
A person independent system for recognition of hand postures used in sign language
Pattern Recognition Letters
Recognition of dynamic gestures in arabic sign language using two stages hierarchical scheme
International Journal of Knowledge-based and Intelligent Engineering Systems
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This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations, and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.